Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Babbar, Varun"'
The performance of machine learning models heavily depends on the quality of input data, yet real-world applications often encounter various data-related challenges. One such challenge could arise when curating training data or deploying the model in
Externí odkaz:
http://arxiv.org/abs/2403.05652
The subject of green AI has been gaining attention within the deep learning community given the recent trend of ever larger and more complex neural network models. Existing solutions for reducing the computational load of training at inference time u
Externí odkaz:
http://arxiv.org/abs/2302.10798
Autor:
Lherondelle, Agathe, Babbar, Varun, Satsangi, Yash, Silavong, Fran, Eloul, Shaltiel, Moran, Sean
This paper presents Topical, a novel deep neural network for repository level embeddings. Existing methods, reliant on natural language documentation or naive aggregation techniques, are outperformed by Topical's utilization of an attention mechanism
Externí odkaz:
http://arxiv.org/abs/2208.09495
Research on human-AI teams usually provides experts with a single label, which ignores the uncertainty in a model's recommendation. Conformal prediction (CP) is a well established line of research that focuses on building a theoretically grounded, ca
Externí odkaz:
http://arxiv.org/abs/2205.01411
Chest Computational Tomography (CT) scans present low cost, speed and objectivity for COVID-19 diagnosis and deep learning methods have shown great promise in assisting the analysis and interpretation of these images. Most hospitals or countries can
Externí odkaz:
http://arxiv.org/abs/2203.13680
The choice of a loss function is an important factor when training neural networks for image restoration problems, such as single image super resolution. The loss function should encourage natural and perceptually pleasing results. A popular choice f
Externí odkaz:
http://arxiv.org/abs/2103.14616
Lightweight Parameter Pruning for Energy-Efficient Deep Learning: A Binarized Gating Module Approach
The subject of green AI has been gaining attention within the deep learning community given the recent trend of ever larger and more complex neural network models. Existing solutions for reducing the computational load of training at inference time u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f7c0ab2b77c38ca4d5209a3e9a208ee
Autor:
Deserno, Thomas M., Park, Brian J., Georgiadis, Antonios, Babbar, Varun, Silavong, Fran, Moran, Sean, Otter, Rob
Publikováno v:
Proceedings of SPIE; April 2022, Vol. 12037 Issue: 1 p1203704-1203704-15
